Learning to make intelligent decisions using an Expert System for the intelligent selection of either PROMETHEE II or the Analytical Hierarchy Process
Learning to make intelligent decisions using an Expert System for the intelligent selection of either PROMETHEE II or the Analytical Hierarchy Process
This paper presents an expert system to select a most suitable discrete Multi Criteria Decision Making (MCDM) method using an approach that analyses problem characteristics, MCDM methods characteristics, risk and uncertainty in inputs and applies sensitivity analysis to the inputs for a decisional problem. Outcomes of this approach can provide decision makers with a suggested candidate method that delivers a robust outcome. Numerical examples are presented where two MCDM methods are compared and one is recommended by calculating the minimum percentage change in criteria weights and performance measures required to alter the ranking of any two alternatives. A MCDM method will be recommended based on a best compromise in minimum percentage change required in inputs to alter the ranking of alternatives.
Discrete, Intelligent, Selection, Risk, Robustness, Uncertainty, Problem characteristics
1303-1316
Haddad, Malik
cdc55972-df6f-492d-8ed0-b022e19b912f
Sanders, David
ad0bcda2-58c6-475e-a6fb-899260c2a6c0
Bausch, Nils
c4e2b4a9-e7df-43c8-a127-cf8e8db06239
Tewkesbury, Giles
f569295c-fb95-4288-a6bc-7d9e5af15b8d
Gegov, Alexander
1016bf16-9fdf-4cdb-b31c-f1b474bf1442
Hassan Sayed, Mohamed
ce323212-f178-4d72-85cf-23cd30605cd8
2019
Haddad, Malik
cdc55972-df6f-492d-8ed0-b022e19b912f
Sanders, David
ad0bcda2-58c6-475e-a6fb-899260c2a6c0
Bausch, Nils
c4e2b4a9-e7df-43c8-a127-cf8e8db06239
Tewkesbury, Giles
f569295c-fb95-4288-a6bc-7d9e5af15b8d
Gegov, Alexander
1016bf16-9fdf-4cdb-b31c-f1b474bf1442
Hassan Sayed, Mohamed
ce323212-f178-4d72-85cf-23cd30605cd8
Haddad, Malik, Sanders, David, Bausch, Nils, Tewkesbury, Giles, Gegov, Alexander and Hassan Sayed, Mohamed
(2019)
Learning to make intelligent decisions using an Expert System for the intelligent selection of either PROMETHEE II or the Analytical Hierarchy Process.
Arai, Kohei, Kapoor, Supriya and Bhatia, Rahul
(eds.)
In Intelligent Systems and Applications: IntelliSys 2018.
vol. 868,
Springer.
.
(doi:10.1007/978-3-030-01054-6_91).
Record type:
Conference or Workshop Item
(Paper)
Abstract
This paper presents an expert system to select a most suitable discrete Multi Criteria Decision Making (MCDM) method using an approach that analyses problem characteristics, MCDM methods characteristics, risk and uncertainty in inputs and applies sensitivity analysis to the inputs for a decisional problem. Outcomes of this approach can provide decision makers with a suggested candidate method that delivers a robust outcome. Numerical examples are presented where two MCDM methods are compared and one is recommended by calculating the minimum percentage change in criteria weights and performance measures required to alter the ranking of any two alternatives. A MCDM method will be recommended based on a best compromise in minimum percentage change required in inputs to alter the ranking of alternatives.
This record has no associated files available for download.
More information
e-pub ahead of print date: 9 November 2018
Published date: 2019
Keywords:
Discrete, Intelligent, Selection, Risk, Robustness, Uncertainty, Problem characteristics
Identifiers
Local EPrints ID: 438256
URI: http://eprints.soton.ac.uk/id/eprint/438256
PURE UUID: 363990f4-7cdf-486e-bd30-3aa0a53d8f9d
Catalogue record
Date deposited: 04 Mar 2020 17:32
Last modified: 17 Mar 2024 04:00
Export record
Altmetrics
Contributors
Author:
Malik Haddad
Author:
David Sanders
Author:
Nils Bausch
Author:
Giles Tewkesbury
Author:
Alexander Gegov
Editor:
Kohei Arai
Editor:
Supriya Kapoor
Editor:
Rahul Bhatia
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics
